IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i24p13524-d696912.html
   My bibliography  Save this article

Energy Retrofit. A Case Study—Santi Romano Dormitory on the Palermo University

Author

Listed:
  • Domenico Curto

    (Department of Engineering, University of Palermo, 90128 Palermo, Italy)

  • Vincenzo Franzitta

    (Department of Engineering, University of Palermo, 90128 Palermo, Italy)

  • Andrea Guercio

    (Department of Engineering, University of Palermo, 90128 Palermo, Italy)

  • Domenico Panno

    (Department of Engineering, University of Palermo, 90128 Palermo, Italy)

Abstract

Electrical and thermal consumption related to buildings, whether civil, commercial, public, or of any other kind, is very much in focus today. With today’s targets for energy savings, reduction of consumption, and environmental impact, it is necessary to carry out energy retrofits to modernize installations and their management. The realization of an effective improvement requires a careful analysis of the case study because each category of building has different requirements such as different load profiles and different installations and needs. This was carried out by studying the electrical and thermal load profiles. A good initial energy audit can provide the retrofit solutions capable of achieving the reduction of energy consumption and the emission of climate-changing gases into the atmosphere. A case study, carried out by the Department of Engineering of Palermo, showed how it is possible to perform an energy retrofit to modernize the energy system of the student dormitory at the University of Palermo. The paper presented a study carried out by programming a series of interlinked calculations in Microsoft Excel. In order to quantify the energy savings of the structure under examination, it is necessary to enter some input data, thanks to which all the formulas implemented in the calculation software were automatically completed. The programming of the calculations makes it possible to carry out an energy retrofit with interventions on the building envelope and the installations. The desire to program an automated calculation by modifying only the input data is intended to replicate a study on other buildings with the same peculiarities. In this way, it is possible to verify which retrofit hypotheses would be useful to upgrade old public administration buildings. In the analyzed case study, 65% of electrical energy and 33% of thermal energy could be saved by replacing generation systems, installing a co-generator, replacing windows, and replacing lamps with LED ones.

Suggested Citation

  • Domenico Curto & Vincenzo Franzitta & Andrea Guercio & Domenico Panno, 2021. "Energy Retrofit. A Case Study—Santi Romano Dormitory on the Palermo University," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13524-:d:696912
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/24/13524/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/24/13524/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    2. Seyedzadeh, Saleh & Pour Rahimian, Farzad & Oliver, Stephen & Rodriguez, Sergio & Glesk, Ivan, 2020. "Machine learning modelling for predicting non-domestic buildings energy performance: A model to support deep energy retrofit decision-making," Applied Energy, Elsevier, vol. 279(C).
    3. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    4. Li, Qing & Zhang, Lianying & Zhang, Limao & Wu, Xianguo, 2021. "Optimizing energy efficiency and thermal comfort in building green retrofit," Energy, Elsevier, vol. 237(C).
    5. Buscemi, Alessandro & Lo Brano, Valerio & Chiaruzzi, Christian & Ciulla, Giuseppina & Kalogeri, Christina, 2020. "A validated energy model of a solar dish-Stirling system considering the cleanliness of mirrors," Applied Energy, Elsevier, vol. 260(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maria Unuigbe & Sambo Lyson Zulu & David Johnston, 2022. "Exploring Factors Influencing Renewable Energy Diffusion in Commercial Buildings in Nigeria: A Grounded Theory Approach," Sustainability, MDPI, vol. 14(15), pages 1-32, August.
    2. Aron Powers & Messiha Saad, 2022. "Building Energy Use: Modeling and Analysis of Lighting Systems—A Case Study," Sustainability, MDPI, vol. 14(20), pages 1-17, October.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Konstantinos Sofias & Zoe Kanetaki & Constantinos Stergiou & Sébastien Jacques, 2023. "Combining CAD Modeling and Simulation of Energy Performance Data for the Retrofit of Public Buildings," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    2. Yanfei Ji & Guangchen Li & Fanghan Su & Yixing Chen & Rongpeng Zhang, 2023. "Retrofit Analysis of City-Scale Residential Buildings in the Hot Summer and Cold Winter Climate Zone," Energies, MDPI, vol. 16(17), pages 1-19, August.
    3. Simon Wenninger & Christian Wiethe, 2021. "Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 223-242, June.
    4. Li, Guanzheng & Li, Bin & Li, Chao & Wang, Shuai, 2023. "State-of-health rapid estimation for lithium-ion battery based on an interpretable stacking ensemble model with short-term voltage profiles," Energy, Elsevier, vol. 263(PE).
    5. Valeria Palladino & Marialaura Di Somma & Carmine Cancro & Walter Gaggioli & Maurizio De Lucia & Marco D’Auria & Michela Lanchi & Fulvio Bassetti & Carla Bevilacqua & Stefano Cardamone & Francesca Nan, 2024. "Innovative Industrial Solutions for Improving the Technical/Economic Competitiveness of Concentrated Solar Power," Energies, MDPI, vol. 17(2), pages 1-34, January.
    6. Mahmoudan, Alireza & Samadof, Parviz & Hosseinzadeh, Siamak & Garcia, Davide Astiaso, 2021. "A multigeneration cascade system using ground-source energy with cold recovery: 3E analyses and multi-objective optimization," Energy, Elsevier, vol. 233(C).
    7. Habtamu Tkubet Ebuy & Hind Bril El Haouzi & Riad Benelmir & Remi Pannequin, 2023. "Occupant Behavior Impact on Building Sustainability Performance: A Literature Review," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    8. Anti Hamburg & Targo Kalamees, 2018. "The Influence of Energy Renovation on the Change of Indoor Temperature and Energy Use," Energies, MDPI, vol. 11(11), pages 1-15, November.
    9. Jakob Carlander & Bahram Moshfegh & Jan Akander & Fredrik Karlsson, 2020. "Effects on Energy Demand in an Office Building Considering Location, Orientation, Façade Design and Internal Heat Gains—A Parametric Study," Energies, MDPI, vol. 13(23), pages 1-22, November.
    10. Alencastro, João & Fuertes, Alba & de Wilde, Pieter, 2018. "The relationship between quality defects and the thermal performance of buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 883-894.
    11. Ahmad, Tanveer & Madonski, Rafal & Zhang, Dongdong & Huang, Chao & Mujeeb, Asad, 2022. "Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    12. Prasanna, Ashreeta & Dorer, Viktor & Vetterli, Nadège, 2017. "Optimisation of a district energy system with a low temperature network," Energy, Elsevier, vol. 137(C), pages 632-648.
    13. Zhong, Shengyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Deng, Shuai & Li, Yang & Hussain, Sajjad & Wang, Xiaoyuan & Zhu, Jiebei, 2021. "Quantitative analysis of information interaction in building energy systems based on mutual information," Energy, Elsevier, vol. 214(C).
    14. Eugene Mohareb & Arman Hashemi & Mehdi Shahrestani & Minna Sunikka-Blank, 2017. "Retrofit Planning for the Performance Gap: Results of a Workshop on Addressing Energy, Health and Comfort Needs in a Protected Building," Energies, MDPI, vol. 10(8), pages 1-17, August.
    15. Isaac Holmes-Gentle & Saurabh Tembhurne & Clemens Suter & Sophia Haussener, 2023. "Kilowatt-scale solar hydrogen production system using a concentrated integrated photoelectrochemical device," Nature Energy, Nature, vol. 8(6), pages 586-596, June.
    16. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    17. Pierryves Padey & Kyriaki Goulouti & Guy Wagner & Blaise Périsset & Sébastien Lasvaux, 2021. "Understanding the Reasons behind the Energy Performance Gap of an Energy-Efficient Building, through a Probabilistic Approach and On-Site Measurements," Energies, MDPI, vol. 14(19), pages 1-15, September.
    18. Ye, Zhongnan & Cheng, Kuangly & Hsu, Shu-Chien & Wei, Hsi-Hsien & Cheung, Clara Man, 2021. "Identifying critical building-oriented features in city-block-level building energy consumption: A data-driven machine learning approach," Applied Energy, Elsevier, vol. 301(C).
    19. Gupta, Rajat & Kotopouleas, Alkis, 2018. "Magnitude and extent of building fabric thermal performance gap in UK low energy housing," Applied Energy, Elsevier, vol. 222(C), pages 673-686.
    20. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:13524-:d:696912. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.